The aim of XINA is to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with co-abundance patterns may have common molecular functions. XINA imports multiple datasets, tags dataset in silico, and combines the data for subsequent subgrouping into multiple clusters. The result is a single output depicting the variation across all conditions. XINA, not only extracts coabundance profiles within and across experiments, but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs.
|Author||Lang Ho Lee <email@example.com> and Sasha A. Singh <firstname.lastname@example.org>|
|Bioconductor views||Network Proteomics RNASeq SystemsBiology|
|Maintainer||Lang Ho Lee <email@example.com> and Sasha A. Singh <firstname.lastname@example.org>|
|Package repository||View on Bioconductor|
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